ROBOTICS & AUTOMATION
Empowering Progress
Up to 25% off Essentials Robotics and Automation titles
Machine Learning Proceedings 1995
Proceedings of the Twelfth International Conference on Machine Learning, Tahoe City, California, July 9-12 1995
- 1st Edition - July 1, 1995
- Editors: Armand Prieditis, Stuart Russell
- Language: English
- Paperback ISBN:9 7 8 - 1 - 5 5 8 6 0 - 3 7 7 - 6
- eBook ISBN:9 7 8 - 1 - 4 8 3 2 - 9 8 6 6 - 5
Machine Learning: Proceedings of the Twelfth International Conference on Machine Learning covers the papers presented at the Twelfth International Conference on Machine Learning… Read more
Purchase options
Institutional subscription on ScienceDirect
Request a sales quoteMachine Learning: Proceedings of the Twelfth International Conference on Machine Learning covers the papers presented at the Twelfth International Conference on Machine Learning (ML95), held at the Granlibakken Resort in Tahoe City, California on July 9-12, 1995. The book focuses on the processes, methodologies, principles, and approaches involved in machine learning, including inductive logic programming algorithms, neural networks, and decision trees. The selection first offers information on the theory and applications of agnostic PAC-learning with small decision trees; reinforcement learning with function approximation; and inductive learning of reactive action models. Discussions focus on inductive logic programming algorithm, collecting instances for learning, residual gradient algorithms, direct algorithms, and learning curves for decision trees of small depth. The text then elaborates on visualizing high-dimensional structure with the incremental grid growing neural network; empirical support for winnow and weighted-majority based algorithms; and automatic selection of split criterion during tree growing based on node location. The manuscript takes a look at learning hierarchies from ambiguous natural language data, learning with rare cases and small disjuncts, learning by observation and practice, and learning collection fusion strategies for information retrieval. The selection is a valuable source of data for mathematicians and researchers interested in machine learning.
Preface
Advisory Committee
Program Committee
Auxiliary Reviewers
Workshops
Tutorials
Schedule
Contributed Papers
On-Line Learning of Binary Lexical Relations Using Two-Dimensional Weighted Majority Algorithms
On Handling Tree-Structured Attributes in Decision Tree Learning
Theory and Applications of Agnostic PAC-Learning with Small Decision Trees
Residual Algorithms: Reinforcement Learning with Function Approximation
Removing the Genetics from the Standard Genetic Algorithm
Inductive Learning of Reactive Action Models
Visualizing High-Dimensional Structure with the Incremental Grid Growing Neural Network
Empirical Support for Winnow and Weighted-Majority Based Algorithms: Results on a Calendar Scheduling Domain
Automatic Selection of Split Criterion During Tree Growing Based on Node Location
A Lexically Based Semantic Bias for Theory Revision
A Comparative Evaluation of Voting and Meta-Learning on Partitioned Data
Fast and Efficient Reinforcement Learning with Truncated Temporal Differences
K*: An Instance-Based Learner Using an Entropic Distance Measure
Fast Effective Rule Induction
Text Categorization and Relational Learning
Protein Folding: Symbolic Refinement Competes with Neural Networks
A Bayesian Analysis of Algorithms for Learning Finite Functions
Committee-Based Sampling For Training Probabilistic Classifiers
Learning Prototypical Concept Descriptions
A Case Study of Explanation-Based Control
Explanation-Based Learning and Reinforcement Learning: A Unified View
Lessons from Theory Revision Applied to Constructive Induction
Supervised and Unsupervised Discretization of Continuous Features
Bounds on the Classification Error of the Nearest Neighbor Rule
Q-Learning for Bandit Problems
Distilling Reliable Information From Unreliable Theories
A Quantitative Study of Hypothesis Selection
Learning Proof Heuristics by Adapting Parameters
Efficient Algorithms for Finding Multi-Way Splits for Decision Trees
Ant-Q: A Reinforcement Learning Approach to the Traveling Salesman Problem
Stable Function Approximation in Dynamic Programming
The Challenge of Revising an Impure Theory
Symbiosis in Multimodal Concept Learning
Tracking the Best Expert
Reinforcement Learning by Stochastic Hill Climbing on Discounted Reward
Automatic Parameter Selection by Minimizing Estimated Error
Error-Correcting Output Coding Corrects Bias and Variance
Learning to Make Rent-to-Buy Decisions with Systems Applications
NewsWeeder: Learning to Filter Netnews
Hill Climbing Beats Genetic Search on a Boolean Circuit Synthesis Problem of Koza's
Case-Based Acquisition of Place Knowledge
Comparing Several Linear-Threshold Learning Algorithms on Tasks Involving Superfluous Attributes
Learning Policies for Partially Observable Environments: Scaling Up
Increasing the Performance and Consistency of Classification Trees by Using the Accuracy Criterion at the Leaves
Efficient Learning with Virtual Threshold Gates
Instance-Based Utile Distinctions for Reinforcement Learning with Hidden State
Efficient Learning from Delayed Rewards through Symbiotic Evolution
Free to Choose: Investigating the Sample Complexity of Active Learning of Real Valued Functions
On Learning Decision Committees
Inferring Reduced Ordered Decision Graphs of Minimum Description Length
On Pruning and Averaging Decision Trees
Efficient Memory-Based Dynamic Programming
Using Multidimensional Projection to Find Relations
Compression-Based Discretization of Continuous Attributes
MDL and Categorical Theories (Continued)
For Every Generalization Action, Is There Really an Equal and Opposite Reaction? Analysis of the Conservation Law for Generalization Performance
Active Exploration and Learning in Real-Valued Spaces Using Multi-Armed Bandit Allocation Indices
Discovering Solutions with Low Kolmogorov Complexity and High Generalization Capability
A Comparison of Induction Algorithms for Selective and Non-Selective Bayesian Classifiers
Retrofitting Decision Tree Classifiers Using Kernel Density Estimation
Automatic Speaker Recognition: An Application of Machine Learning
An Inductive Learning Approach to Prognostic Prediction
TD Models: Modeling the World at a Mixture of Time Scales
Learning Collection Fusion Strategies for Information Retrieval
Learning by Observation and Practice: An Incremental Approach for Planning Operator Acquisition
Learning with Rare Cases and Small Disjuncts
Horizontal Generalization
Learning Hierarchies from Ambiguous Natural Language Data
Invited Talks (Abstracts Only)
Machine Learning and Information Retrieval
Learning With Bayesian Networks
Learning for Automotive Collision Avoidance and Autonomous Control
Author Index
- No. of pages: 400
- Language: English
- Edition: 1
- Published: July 1, 1995
- Imprint: Morgan Kaufmann
- Paperback ISBN: 9781558603776
- eBook ISBN: 9781483298665
Read Machine Learning Proceedings 1995 on ScienceDirect